import numpy as np
import os, imageio
import torch
import scipy
import cv2
import pdb
from shutil import copy
from subprocess import check_output
    
########## Slightly modified version of LLFF data loading code
##########  see https://github.com/Fyusion/LLFF for original
def imread(f):
    if f.endswith('png'):
        return imageio.imread(f, ignoregamma=True)
    else:
        return imageio.imread(f)

def depthread(path):
    with open(path, "rb") as fid:
        width, height, channels = np.genfromtxt(fid, delimiter="&", max_rows=1,
                                                usecols=(0, 1, 2), dtype=int)
        fid.seek(0)
        num_delimiter = 0
        byte = fid.read(1)
        while True:
            if byte == b"&":
                num_delimiter += 1
                if num_delimiter >= 3:
                    break
            byte = fid.read(1)
        array = np.fromfile(fid, np.float32)
    array = array.reshape((width, height, channels), order="F")
    return np.transpose(array, (1, 0, 2)).squeeze()


def _minify(basedir, factors=[], resolutions=[]):
    needtoload = False
    for r in factors:
        imgdir = os.path.join(basedir, 'images_{}'.format(r))
        if not os.path.exists(imgdir):
            needtoload = True
    for r in resolutions:
        imgdir = os.path.join(basedir, 'images_{}x{}'.format(r[1], r[0]))
        if not os.path.exists(imgdir):
            needtoload = True
    if not needtoload:
        return

    imgdir = os.path.join(basedir, 'images')
    imgs = [os.path.join(imgdir, f) for f in sorted(os.listdir(imgdir))]
    imgs = [f for f in imgs if any([f.endswith(ex) for ex in ['JPG', 'jpg', 'jpeg', 'png', 'jpeg', 'PNG']])]
    imgdir_orig = imgdir

    wd = os.getcwd()

    for r in factors + resolutions:
        if isinstance(r, int):
            name = 'images_{}'.format(r)
            resizearg = '{}%'.format(100./r)
        else:
            name = 'images_{}x{}'.format(r[1], r[0])
            resizearg = '{}x{}'.format(r[1], r[0])
        imgdir = os.path.join(basedir, name)
        if os.path.exists(imgdir):
            print("Image folder exists, do not call the resize function.")
            continue
        os.makedirs(imgdir, exist_ok=True)
        print('Minifying', r, basedir)
        # check_output('cp {}/* {}'.format(imgdir_orig, imgdir), shell=True)
        ext = imgs[0].split('.')[-1]
        for idx, one_img_p in enumerate(imgs):
            one_img = cv2.imread(one_img_p)
            ori_h, ori_w = one_img.shape[0], one_img.shape[1]
            if isinstance(r, int):
                target_h, target_w = int(ori_h / r), int(ori_w / r)
            else:
                target_h, target_w = r[0], r[1]
            resized = cv2.resize(one_img, (target_w, target_h), interpolation = cv2.INTER_AREA)
            target_img_p = one_img_p.replace(imgdir_orig, imgdir)
            cv2.imwrite(target_img_p, resized)
        # args = ' '.join(['convert mogrify', '-resize', resizearg, '-format', 'png', '*.{}'.format(ext)])
        # print(args)
        # os.chdir(imgdir)
        # check_output(args, shell=True)
        # os.chdir(wd)

        # if ext != 'png':
        #     check_output('rm {}/*.{}'.format(imgdir, ext), shell=True)
        #     print('Removed duplicates')
        print('Done')


def _load_data(basedir, factor=None, width=None, height=None, load_imgs=True, load_depths=False):

    poses_arr = np.load(os.path.join(basedir, 'poses_bounds.npy'))
    if poses_arr.shape[1] == 17:
        poses = poses_arr[:, :-2].reshape([-1, 3, 5]).transpose([1,2,0])
    elif poses_arr.shape[1] == 14:
        poses = poses_arr[:, :-2].reshape([-1, 3, 4]).transpose([1,2,0])
    else:
        raise NotImplementedError
    bds = poses_arr[:, -2:].transpose([1,0])
    img0 = [os.path.join(basedir, 'images', f) for f in sorted(os.listdir(os.path.join(basedir, 'images'))) \
            if f.endswith('JPG') or f.endswith('jpg') or f.endswith('jpeg') or f.endswith('png')][0]
    sh = imageio.imread(img0).shape

    sfx = ''

    if height is not None and width is not None:
        _minify(basedir, resolutions=[[height, width]])
        sfx = '_{}x{}'.format(width, height)
    elif factor is not None and factor != 1:
        sfx = '_{}'.format(factor)
        _minify(basedir, factors=[factor])
        factor = factor
    elif height is not None:
        factor = sh[0] / float(height)
        width = int(sh[1] / factor)
        _minify(basedir, resolutions=[[height, width]])
        sfx = '_{}x{}'.format(width, height)
    elif width is not None:
        factor = sh[1] / float(width)
        height = int(sh[0] / factor)
        _minify(basedir, resolutions=[[height, width]])
        sfx = '_{}x{}'.format(width, height)
    else:
        factor = 1
    imgdir = os.path.join(basedir, 'images' + sfx)
    print(f'Loading images from {imgdir}')
    if not os.path.exists(imgdir):
        print(imgdir, 'does not exist, returning' )
        return

    imgfiles = [os.path.join(imgdir, f) for f in sorted(os.listdir(imgdir)) if f.endswith('JPG') or f.endswith('jpg') or f.endswith('png')]
    if poses.shape[-1] != len(imgfiles):
        print()
        print( 'Mismatch between imgs {} and poses {} !!!!'.format(len(imgfiles), poses.shape[-1]) )
        names = set(name[:-4] for name in np.load(os.path.join(basedir, 'poses_names.npy')))
        assert len(names) == poses.shape[-1]
        print('Below failed files are skip due to SfM failure:')
        new_imgfiles = []
        for i in imgfiles:
            fname = os.path.split(i)[1][:-4]
            if fname in names:
                new_imgfiles.append(i)
            else:
                print('==>', i)
        imgfiles = new_imgfiles

    if len(imgfiles) < 3:
        print('Too few images...')
        import sys; sys.exit()

    sh = imageio.imread(imgfiles[0]).shape
    if poses.shape[1] == 4:
        poses = np.concatenate([poses, np.zeros_like(poses[:,[0]])], 1)
        poses[2, 4, :] = np.load(os.path.join(basedir, 'hwf_cxcy.npy'))[2]
    poses[:2, 4, :] = np.array(sh[:2]).reshape([2, 1])
    poses[2, 4, :] = poses[2, 4, :] * 1./factor

    if not load_imgs:
        return poses, bds
    imgs = imgs = [imread(f)[...,:3]/255. for f in imgfiles]
    imgs = np.stack(imgs, -1)

    print('Loaded image data', imgs.shape, poses[:,-1,0])
    if not load_depths:
        return poses, bds, imgs

    depthdir = os.path.join(basedir, 'stereo', 'depth_maps')
    assert os.path.exists(depthdir), f'Dir not found: {depthdir}'

    depthfiles = [os.path.join(depthdir, f) for f in sorted(os.listdir(depthdir)) if f.endswith('.geometric.bin')]
    assert poses.shape[-1] == len(depthfiles), 'Mismatch between imgs {} and poses {} !!!!'.format(len(depthfiles), poses.shape[-1])

    depths = [depthread(f) for f in depthfiles]
    depths = np.stack(depths, -1)
    print('Loaded depth data', depths.shape)
    return poses, bds, imgs, depths


def normalize(x):
    return x / np.linalg.norm(x)

def viewmatrix(z, up, pos):
    vec2 = normalize(z)
    vec1_avg = up
    vec0 = normalize(np.cross(vec1_avg, vec2))
    vec1 = normalize(np.cross(vec2, vec0))
    m = np.stack([vec0, vec1, vec2, pos], 1)
    return m

def ptstocam(pts, c2w):
    tt = np.matmul(c2w[:3,:3].T, (pts-c2w[:3,3])[...,np.newaxis])[...,0]
    return tt

def poses_avg(poses):
    assert poses.shape[-1] == 5, "HWF must be given in the pose matrix!"
    hwf = poses[0, :3, -1:]
    center = poses[:, :3, 3].mean(0)
    vec2 = normalize(poses[:, :3, 2].sum(0))
    up = poses[:, :3, 1].sum(0)
    c2w = np.concatenate([viewmatrix(vec2, up, center), hwf], 1)
    return c2w


def render_path_spiral(c2w, up, rads, focal, zdelta, zrate, rots, N):
    render_poses = []
    rads = np.array(list(rads) + [1.])
    hwf = c2w[:,4:5]

    for theta in np.linspace(0., 2. * np.pi * rots, N+1)[:-1]:
        c = np.dot(c2w[:3,:4], np.array([np.cos(theta), -np.sin(theta), -np.sin(theta*zrate)*zdelta, 1.]) * rads) 
        z = normalize(c - np.dot(c2w[:3,:4], np.array([0,0,-focal, 1.])))
        render_poses.append(np.concatenate([viewmatrix(z, up, c), hwf], 1))
    return render_poses


def recenter_poses(poses):
    poses_ = poses+0
    bottom = np.reshape([0,0,0,1.], [1,4])
    c2w = poses_avg(poses)
    c2w = np.concatenate([c2w[:3,:4], bottom], -2)
    bottom = np.tile(np.reshape(bottom, [1,1,4]), [poses.shape[0],1,1])
    poses = np.concatenate([poses[:,:3,:4], bottom], -2)

    poses = np.linalg.inv(c2w) @ poses
    poses_[:,:3,:4] = poses[:,:3,:4]
    poses = poses_
    return poses


def rerotate_poses(poses):
    poses = np.copy(poses)
    centroid = poses[:,:3,3].mean(0)

    poses[:,:3,3] = poses[:,:3,3] - centroid

    # Find the minimum pca vector with minimum eigen value
    x = poses[:,:,3]
    mu = x.mean(0)
    cov = np.cov((x-mu).T)
    ev , eig = np.linalg.eig(cov)
    cams_up = eig[:,np.argmin(ev)]
    if cams_up[1] < 0:
        cams_up = -cams_up

    # Find rotation matrix that align cams_up with [0,1,0]
    R = scipy.spatial.transform.Rotation.align_vectors(
            [[0,1,0]], cams_up[None])[0].as_matrix()

    # Apply rotation and add back the centroid position
    poses[:,:3,:3] = R @ poses[:,:3,:3]
    poses[:,:3,[3]] = R @ poses[:,:3,[3]]
    poses[:,:3,3] = poses[:,:3,3] + centroid
    return poses

#####################


def spherify_poses(poses, bds, depths):

    p34_to_44 = lambda p : np.concatenate([p, np.tile(np.reshape(np.eye(4)[-1,:], [1,1,4]), [p.shape[0], 1,1])], 1)

    rays_d = poses[:,:3,2:3]
    rays_o = poses[:,:3,3:4]

    def min_line_dist(rays_o, rays_d):
        A_i = np.eye(3) - rays_d * np.transpose(rays_d, [0,2,1])
        b_i = -A_i @ rays_o
        pt_mindist = np.squeeze(-np.linalg.inv((np.transpose(A_i, [0,2,1]) @ A_i).mean(0)) @ (b_i).mean(0))
        return pt_mindist

    pt_mindist = min_line_dist(rays_o, rays_d)

    center = pt_mindist
    up = (poses[:,:3,3] - center).mean(0)

    vec0 = normalize(up)
    vec1 = normalize(np.cross([.1,.2,.3], vec0))
    vec2 = normalize(np.cross(vec0, vec1))
    pos = center
    c2w = np.stack([vec1, vec2, vec0, pos], 1)

    poses_reset = np.linalg.inv(p34_to_44(c2w[None])) @ p34_to_44(poses[:,:3,:4])

    radius = np.sqrt(np.mean(np.sum(np.square(poses_reset[:,:3,3]), -1)))

    sc = 1./radius
    poses_reset[:,:3,3] *= sc
    bds *= sc
    radius *= sc
    depths *= sc

    poses_reset = np.concatenate([poses_reset[:,:3,:4], np.broadcast_to(poses[0,:3,-1:], poses_reset[:,:3,-1:].shape)], -1)

    return poses_reset, radius, bds, depths


def load_llff_data(basedir, factor=8, width=None, height=None,
                   recenter=True, rerotate=True,
                   bd_factor=.75, spherify=False, path_zflat=False, load_depths=False,
                   movie_render_kwargs={}):
    poses, bds, imgs, *depths = _load_data(basedir, factor=factor, width=width, height=height,
                                           load_depths=load_depths) # factor=8 downsamples original imgs by 8x
    # poses: [3, 5, N], bds: [2, N], imgs: [H, W, 3, N], depths: []
    print('Loaded', basedir, bds.min(), bds.max())
    if load_depths:
        depths = depths[0]
    else:
        depths = 0
    # Correct rotation matrix ordering and move variable dim to axis 0
    poses = np.concatenate([poses[:, 1:2, :], -poses[:, 0:1, :], poses[:, 2:, :]], 1)
    poses = np.moveaxis(poses, -1, 0).astype(np.float32)
    imgs = np.moveaxis(imgs, -1, 0).astype(np.float32)
    images = imgs
    bds = np.moveaxis(bds, -1, 0).astype(np.float32)
    # Rescale if bd_factor is provided
    if bds.min() < 0 and bd_factor is not None:
        print('Found negative z values from SfM sparse points!?')
        print('Please try bd_factor=None. This program is terminating now!')
        import sys; sys.exit()
    sc = 1. if bd_factor is None else 1./(bds.min() * bd_factor)
    poses[:,:3,3] *= sc
    bds *= sc
    depths *= sc
    if recenter:
        poses = recenter_poses(poses)
    if spherify:
        poses, radius, bds, depths = spherify_poses(poses, bds, depths)
        if rerotate:
            poses = rerotate_poses(poses)

        ### generate spiral poses for rendering fly-through movie
        centroid = poses[:,:3,3].mean(0)
        radcircle = movie_render_kwargs.get('scale_r', 1) * np.linalg.norm(poses[:,:3,3] - centroid, axis=-1).mean()
        centroid[0] += movie_render_kwargs.get('shift_x', 0)
        centroid[1] += movie_render_kwargs.get('shift_y', 0)
        centroid[2] += movie_render_kwargs.get('shift_z', 0)
        new_up_rad = movie_render_kwargs.get('pitch_deg', 0) * np.pi / 180
        target_y = radcircle * np.tan(new_up_rad)

        render_poses = []

        for th in np.linspace(0., 2.*np.pi, 200):
            camorigin = np.array([radcircle * np.cos(th), 0, radcircle * np.sin(th)])
            if movie_render_kwargs.get('flip_up', False):
                up = np.array([0,1.,0])
            else:
                up = np.array([0,-1.,0])
            vec2 = normalize(camorigin)
            vec0 = normalize(np.cross(vec2, up))
            vec1 = normalize(np.cross(vec2, vec0))
            pos = camorigin + centroid
            # rotate to align with new pitch rotation
            lookat = -vec2
            lookat[1] = target_y
            lookat = normalize(lookat)
            vec2 = -lookat
            vec1 = normalize(np.cross(vec2, vec0))

            p = np.stack([vec0, vec1, vec2, pos], 1)

            render_poses.append(p)

        render_poses = np.stack(render_poses, 0)
        render_poses = np.concatenate([render_poses, np.broadcast_to(poses[0,:3,-1:], render_poses[:,:3,-1:].shape)], -1)

    else:

        c2w = poses_avg(poses)
        print('recentered', c2w.shape)
        print(c2w[:3,:4])

        ## Get spiral
        # Get average pose
        up = normalize(poses[:, :3, 1].sum(0))

        # Find a reasonable "focus depth" for this dataset
        close_depth, inf_depth = bds.min()*.9, bds.max()*5.
        dt = .75
        mean_dz = 1./(((1.-dt)/close_depth + dt/inf_depth))
        focal = mean_dz * movie_render_kwargs.get('scale_f', 1)

        # Get radii for spiral path
        zdelta = movie_render_kwargs.get('zdelta', 0.5)
        zrate = movie_render_kwargs.get('zrate', 1.0)
        tt = poses[:,:3,3] # ptstocam(poses[:3,3,:].T, c2w).T
        rads = np.percentile(np.abs(tt), 90, 0) * movie_render_kwargs.get('scale_r', 1)
        c2w_path = c2w
        N_views = 120
        N_rots = movie_render_kwargs.get('N_rots', 1)
        if path_zflat:
#             zloc = np.percentile(tt, 10, 0)[2]
            zloc = -close_depth * .1
            c2w_path[:3,3] = c2w_path[:3,3] + zloc * c2w_path[:3,2]
            rads[2] = 0.
            N_rots = 1
            N_views/=2

        # Generate poses for spiral path
        render_poses = render_path_spiral(c2w_path, up, rads, focal, zdelta, zrate=zrate, rots=N_rots, N=N_views)

    render_poses = torch.Tensor(render_poses)

    c2w = poses_avg(poses)
    print('Data:')
    print(poses.shape, images.shape, bds.shape)
    # this part is written by DVGO.
    dists = np.sum(np.square(c2w[:3,3] - poses[:,:3,3]), -1)
    i_test = np.argmin(dists)
    print('HOLDOUT view is', i_test) 

    images = images.astype(np.float32)
    poses = poses.astype(np.float32)
    return images, depths, poses, bds, render_poses, i_test

